WO2018133043A1 - Quantizer and quantization method - Google Patents

Abstract

Provided are a quantizer and a quantization method. The quantizer comprises a quantization selection circuit (10) and at least two quantization circuits (1). Each quantization circuit (1) corresponds to a quantization method. An output terminal (12) of the quantization selection circuit (10) is connected to an input terminal (21) of the quantization circuit (1). The quantization selection circuit (10) is used to compare a predicted residual e_l of first information source data x_l with a threshold value to determine a segment to which e_l belongs, determine, according to the segment to which e_l belongs, a target quantization method for quantizing e_l, and output e_l to a target quantization circuit used to perform the target quantization method, wherein e_l is a difference between x₁ and a predicted value (I) of x₁, and (I) is a predicted value obtained by performing linear prediction on x_l. The target quantization circuit is used to quantize e_l to obtain a first quantization result u_l. The method can make full use of correlation characteristics of information source data and a distribution rule thereof, and can reduce the average number of bits required for encoding and a required bandwidth for data transmission while providing a desired quantization signal-to-noise ratio.

Description

Quantizer and quantization method Technical field

The present invention relates to the field of communications technologies, and in particular, to a quantizer and a quantization method.

Background technique

In the actual communication scenario, most of the sources belong to the memory source. For the memory source, there is a statistical correlation between the source data output by the source. If the statistical association between the source data is utilized, Reduce redundancy in data transmission, and play the role of data compression, thereby improving bandwidth utilization.

Predictive coding is based on this idea. The predictive coding does not directly encode the source data, but predicts the source data and encodes the difference between the predicted value and the source data. In practical applications, DPCM (Differential Pulse Code Modulation) is a commonly used linear prediction-based coding architecture. Its working principle is as follows: predict the value of the currently input source data based on historical source data. And calculating the difference between the predicted value and the actual source data, and quantizing and encoding the difference and then transmitting the digital signal.

When the above difference is quantized, the uniform quantization method can be selected. Assuming that the working range of the quantizer is (-V, +V), the uniform quantizer divides the range of (-V, +V) evenly into M The length is Δ=2V/M. The midpoint of the interval is obtained as a quantization level, and the above difference is quantized. However, uniform quantization requires more bandwidth if the expected signal-to-noise ratio is obtained.

CPRI (Common Public Radio Interface) is a protocol that regulates the interface standard between radio frequency control equipment and radio frequency equipment in a base station. Data compression techniques that reduce the transmission load on the CPRI interface, including time domain compression schemes (such as downsampling rate, nonlinear quantization, quadrature modulated signal data compression, etc.) and frequency domain compression schemes (such as subcarrier compression).

The starting point of the frequency domain compression scheme is that the source signal has a large amount of redundancy in the frequency domain; however, the frequency domain compression may result in complex constellation mapping, increase interface logic design and processing complexity, and is not achievable.

Another solution is the time domain compression scheme. A representative time domain compression scheme is based on the characteristics of the source signal, which eliminates redundancy by interpolation, low-pass filtering and down-sampling, and also greatly reduces the number of symbols. In addition, for the characteristics of the signal dynamic range is large, the data is segmented and scaled to ensure that the same data bit width can represent more signals with larger amplitude. However, the time domain compression scheme does not make full use of the correlation characteristics between the sources and the distribution law of the source, and the compression ratio has room for improvement.

Summary of the invention

Embodiments of the present invention provide a quantizer and quantization method for compressing a digital signal to reduce a bandwidth required for transmission.

A first aspect of the embodiment of the present invention discloses a quantizer, including:

a quantization selection circuit and at least two quantization circuits; each quantization circuit corresponds to a quantization mode;

An output end of the quantization selection circuit is connected to an input end of the quantization circuit;

The quantization selection circuit for comparing a first data source e x _{l L} prediction residuals to a threshold to determine whether the segment belongs to _L e, e is determined according to the segment belongs to the _L e quantizing the target quantization _l manner, and the target quantization circuit E _l is output to the target for performing the quantization mode; the said X E _{l l l} the predicted value X

Difference, said

a prediction value obtained by linearly predicting the x _l ; the e _l reaches the quantization selection circuit via an input of the quantization selection circuit; the e _l reaches the output via an output of the quantization selection circuit Target quantization circuit;

The target quantization circuit is configured to quantize the e _l to obtain a first quantization result u _l .

In this embodiment, the prediction residual is segment-quantized, that is, the matching quantization method is selected for each segment according to the distribution rule of the prediction residual, and the data compression rate can be improved under the premise of satisfying the requirement of the quantized signal to noise ratio. , that is, reduce the bandwidth required for transmission.

As an optional implementation manner, the quantizer further includes a threshold calculation circuit;

An output end of the at least two quantization circuits is connected to an input end of the threshold calculation circuit; an output end of the threshold calculation circuit is connected to an input end of the quantization selection circuit;

The threshold value calculating circuit according to a second source data x _l-1, x _l-1 the prediction residuals e _l-1, e _l-1 of the second quantization result and the target u _l-1 quantization noise ratio SNR _t determining the threshold, x _l-1 of the source data is a time prior to said x _l; e _l-1 is the x _l-1 and the x _l-1 of the Predictive value

Difference, said

a predicted value obtained by linearly predicting the x _l-1 ; wherein the x _l-1 , the e _l-1 , the u _l-1, and the SNR _{t are} subjected to the threshold calculation circuit The input arrives at the threshold calculation circuit; the threshold reaches the quantization selection circuit via an output of the threshold calculation circuit.

In this embodiment, the threshold for predicting the residual difference segment is determined according to the requirements of the target quantized signal to noise ratio to ensure that the quantized data can achieve an ideal signal to noise ratio.

As an optional implementation manner, the threshold is a first threshold group consisting of _n-1 values respectively of τ ₁ ～τ _n-1 , and the τ ₁ ～τ _n-1 will take the prediction residual The value range is divided into n segments; the first threshold group is an average of n _τ threshold groups; wherein an interval between every two thresholds of the threshold group is determined according to a distribution of the prediction residuals; the prediction The distribution of residuals is determined based on prior knowledge;

The threshold value calculating circuit according to a second source data x _l-1, x _l-1 the prediction residuals e _l-1, e _l-1 of the second quantization result and the target u _l-1 The quantized signal to noise ratio SNR _t determines the threshold, including:

Calculate the threshold group:

According to the x _l-1, x _l-1 the prediction residuals e _l-1 and the second quantization result u _l-1 calculates the transmission end signal to noise ratio

Determining whether the SNR _q is greater than the SNR _t ; if SNR _q > SNR _t , increasing each threshold in the threshold group by δ; if SNR _q < SNR _t , decreasing each threshold in the threshold group δ;

_N τ obtain results set threshold values, averaging the _n τ threshold values to obtain the first set of thresholds.

In this embodiment, the threshold can be dynamically modified according to the signal-to-noise ratio of the transmitting end to ensure that the quantized data can reach an ideal signal-to-noise ratio.

As an optional implementation manner, the quantizer further includes a subtractor;

An output end of the subtractor is connected to an input end of the quantization selection circuit;

The subtractor is configured to compare the first source data x _l with the predicted value of the x _l

Subtracted to obtain the prediction residual x _l e _l; the

Is the predicted value obtained by the linear prediction x _l; wherein said x _l and the

Via the subtractor reaches the input of a subtracter; e _l via said output terminal of said subtractor reaches the quantization selection circuit.

In this embodiment, the transmission source data is converted into a transmission pair source by using correlation characteristics between source data. The data predicts the residual of the prediction, thereby reducing the data bit width of the transmission and increasing the data compression rate.

As an optional implementation manner, the quantizer further includes a linear predictor;

An output of the linear predictor is coupled to an input of the subtractor;

The linear predictor is configured to calculate a predicted value of the first source data x _l according to the second source data x _l-1 and the second quantization result u _l-1

Wherein the x _l-1 and the u _l-1 reach the linear predictor via an input of the linear predictor;

The subtractor is reached via the output of the linear predictor.

In this embodiment, the source data is linearly predicted by using correlation characteristics between the source data, and when the data is transmitted, the transmission source data is converted into a prediction residual for predicting the source data, thereby Reduce the data bit width of the transmission and increase the data compression rate.

As an optional implementation manner, the linear predictor includes:

a prediction coefficient calculation circuit and a linear prediction circuit;

The prediction coefficient calculation circuit is configured to calculate a prediction coefficient, where the prediction coefficient includes a first prediction coefficient, and the first prediction coefficient is a prediction coefficient that performs the linear prediction on the first source data x _l ;

The calculating the prediction coefficient includes:

Calculating a correlation function by using the N _block source data before the x _l ;

Obtaining f related functions to average the f correlation functions to obtain an average correlation function, and establishing a relationship between the average correlation function and the prediction coefficients by a minimum mean square error criterion to obtain the prediction coefficient;

The first prediction coefficient reaches the linear prediction circuit via an output end of the prediction coefficient calculation circuit;

The linear prediction circuit is configured to predict a value according to the first prediction coefficient and the second source data

And the u _l-1 calculation

include:

Calculate the

a product of the sum of the u _l-1 and the first prediction coefficient as an intermediate amount;

Summing the K intermediate quantities after obtaining the K intermediate quantities to obtain the

Wherein K is an order of linear prediction;

Said

And the u _l-1 arrives at the linear prediction circuit via an input of the linear prediction circuit;

The subtractor is reached via the output of the linear prediction circuit.

In this embodiment, when calculating the correlation function, the method of calculating the correlation function by segmenting the input source data is performed, and the correlation function is updated in stages, thereby reducing the data complexity of performing correlation function calculation at the data transmitting end.

As an optional implementation manner, the quantizer further includes a framing circuit;

An output end of the quantization selection circuit is connected to an input end of the framing circuit; an output end of the target quantization circuit is connected to an input end of the framing circuit; and an output end of the prediction coefficient calculation circuit is connected to the framing frame The input of the circuit;

The quantization selection circuit is further configured to obtain identification information of the position of u _l; the location identification information u _l e _l is the segment belongs; u _l position of the identification information and the thresholds by An output of the quantization selection circuit reaches the framing circuit;

The u _l reaches the framing circuit via an output end of the target quantization circuit;

The first prediction coefficient reaches the framing circuit via an output end of the prediction coefficient calculation circuit;

The framing circuit is configured to form the data frame of the u _l , the location identifier information of the u _l , the threshold, and the first prediction coefficient for transmission.

In this embodiment, the quantization result, the location identification information, the threshold, and the prediction coefficient are combined into a data frame for transmission. After receiving the data frame, the data receiving end can restore the prediction residual of the source data according to the quantization result, the position identification information and the threshold, and use the prediction coefficient to obtain the predicted value of the source data, and predict the residual and The predicted values are added to restore the source data.

A second aspect of the embodiments of the present invention discloses a quantization method, including:

Obtaining a prediction residual e _{l of the} first source data x _l and a threshold; the e _l is a predicted value of the x _l and the x _l

Difference, said

a predicted value obtained by linearly predicting the x _l ;

The e _l to the threshold to determine the segment belongs e _l, e _l of the determination of the segment belongs e _l quantizing the target quantization According;

The e _l is quantized by the target quantization method to obtain a first quantization result u _l .

As an optional implementation manner, before the comparing the prediction residual e _{l of the} first source data x ₁ with the threshold to determine the segment to which the e _l belongs, the method further includes:

Determining according to the second source data x _l-1 , the prediction residual e _{l-1 of} the x _l-1 , the second quantization result u _{l-1 of} the e _l-1 , and the target quantized signal to noise ratio SNR _t the threshold value; x _l-1 of the source data for the previous time the x _l, e _l-1 is the x _{l 1-a} and the predicted value of x _l-1

Difference, said

The predicted value obtained by linearly predicting the x _l-1 .

As an optional implementation manner, the threshold is a first threshold group consisting of _n-1 values respectively of τ ₁ ～τ _n-1 , and the τ ₁ ～τ _n-1 will take the prediction residual The value range is divided into n segments; the first threshold group is an average of n _τ threshold groups; wherein an interval between every two thresholds of the threshold group is determined according to a distribution of the prediction residuals; the prediction The distribution of residuals is determined based on prior knowledge;

The second quantization result e _l-1 according to the second source data x _l-1 , the x _{l- 1} , the second quantization result u _{l-1 of} the e _l-1 , and the target quantized signal to noise ratio SNR _t determining the threshold, including:

Calculate the threshold group:

According to the x _l-1, x _l-1 the prediction residuals e _l-1 and the second quantization result u _l-1 calculates the transmission end signal to noise ratio

Determining whether the SNR _q is greater than the SNR _t ; if SNR _q > SNR _t , increasing each threshold in the threshold group by δ; if SNR _q < SNR _t , decreasing each threshold in the threshold group δ;

_N τ obtain results set threshold values, averaging the _n τ threshold values to obtain the first set of thresholds.

As an optional implementation manner, before the comparing the prediction residual e _{l of the} first source data x ₁ with the threshold to determine the segment to which the e _l belongs, the method further includes:

Predicting the first source data x _l and the x _l

Subtracted to obtain the prediction residual x _l e _l; the

The predicted value obtained by linearly predicting the x _l .

As an optional implementation manner, the first source data x _l and the predicted value of the x _l are

Before subtracting to obtain the prediction residual e _{l of} the x _l , the method further includes:

Calculating a predicted value of the first source data x _l according to the second source data x _l-1 and the second quantization result u _l-1

As an optional implementation manner, the calculating, according to the second source data x _l-1 and the second quantization result u _l-1 , the predicted value of the first source data x _l

Previously, the method further includes:

Calculating a prediction coefficient, the prediction coefficient including a first prediction coefficient, the first prediction coefficient being a prediction coefficient for predicting the first source data x _l ;

The calculating the prediction coefficient includes:

Calculating a correlation function by using the N _block source data before the x _l ;

Obtaining f related functions to average the f correlation functions to obtain an average correlation function, and establishing a relationship between the average correlation function and the prediction coefficients by a minimum mean square error criterion to obtain the prediction coefficient;

Calculating the predicted value of the first source data x _l according to the second source data x _l-1 and the second quantization result u _l-1

include:

Calculate the

a product of the sum of the u _l-1 and the first prediction coefficient as an intermediate amount;

Summing the K intermediate quantities after obtaining the K intermediate quantities to obtain the

Where K is the order of linear prediction.

As an optional implementation manner, the method further includes:

Obtaining the location identifier information of the u _l and the threshold; the location identifier information of the u _l is a segment to which the e _l belongs;

The u _l , the location identification information of the u _l , the threshold, and the first prediction coefficient are combined into a data frame for transmission.

It can be seen from the above technical solutions that the embodiments of the present invention have the following advantages:

In the embodiment of the present invention, the segment to which the prediction residual needs to be quantized is determined first, and then the target quantization mode for quantifying the prediction residual is determined according to the segment to which the prediction residual belongs, and finally the target residual quantization method is used to perform the prediction residual. Quantization to obtain the quantized result, so that the correlation between the source data and the distribution law of the source data can be fully utilized, and when the expected quantized signal to noise ratio is obtained, the average number of bits required for encoding can be reduced, thereby reducing the data. The bandwidth required for transmission.

DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background art, the drawings to be used in the embodiments of the present invention or the background art will be described below.

1 is a schematic structural diagram of a quantizer according to an embodiment of the present invention;

2 is a schematic structural diagram of another quantizer according to an embodiment of the present invention;

FIG. 3 is a schematic flowchart diagram of a quantization method according to an embodiment of the present disclosure;

4 is a schematic flowchart diagram of another quantization method according to an embodiment of the present invention;

4a is a schematic structural diagram of a linear predictor according to an embodiment of the present invention;

4b is a schematic diagram of a probability density of prediction residuals according to an embodiment of the present invention;

FIG. 5 is a schematic flowchart of calculating an initial value of τ ₁ according to an embodiment of the present invention;

FIG. 6 is a schematic flowchart diagram of a threshold update method according to an embodiment of the present invention.

detailed description

The embodiments of the present invention are described below in conjunction with the accompanying drawings in the embodiments of the present invention.

The embodiment of the invention provides a quantizer and a quantization method, which can fully utilize the correlation characteristics between the source data and the distribution law of the source data, and reduce the average required for encoding when obtaining the expected quantized signal to noise ratio. The number of bits, which reduces the bandwidth required for data transmission.

Please refer to FIG. 1. FIG. 1 is a schematic structural diagram of a quantizer disclosed in an embodiment of the present invention. As shown in FIG. 1, the quantizer described in this embodiment includes a quantization selection circuit 10 and a quantization circuit group 20. The quantization circuit group 20 includes at least two quantization circuits, each of which corresponds to a quantization method, wherein:

The quantization selection circuit 10 includes an input terminal 11 and an output terminal 12; the quantization circuit group 20 includes an input terminal 21 and an output terminal 22.

The output 12 of the quantization selection circuit 10 is connected to the input 21 of the quantization circuit group 20.

In the embodiment of the present invention, the source data including the first source data x _l and the second source data x _l-1 is digitized data, for example, when the data is transmitted, the data is sent. The quadrature modulated signal that needs to be transmitted at the end.

Embodiments of the present invention, the quantization circuit 10 to select the first source data x _l e _l prediction residuals to a threshold to determine the segment belongs, e _l, e _l quantizing determined according to the segment belongs e _l The target quantization method, and outputs e _l to the target quantization circuit for performing the target quantization mode. Where e _l is the predicted value of x _l and x _l

Difference,

Is the predicted value of the linear prediction is obtained for x _l; e _l quantized selection input of the circuit 10 is 11 reaches the quantization selection circuit 10; e _l quantized selection target quantization circuit output of the circuit 10 is 12 to the quantization circuit group 20 .

As an optional implementation manner, the quantization circuit group 20 may be configured with a switching circuit such that the input end of the target quantization circuit is connected to the input end 21 of the quantization circuit group 20, so that the target quantization circuit can receive the quantization selection. The output _{l of} circuit 10 is e _l .

After receiving e _l , the target quantization circuit in the quantization circuit group 20 quantizes e _l to obtain a first quantization result u _l .

In the embodiment of the present invention, the threshold may be a set of thresholds pre-stored in the quantization selection circuit 10; or, the quantization selection circuit 10 pre-stores a plurality of sets of selectable thresholds, and selects a set of selectable thresholds every preset time interval. The threshold is updated; alternatively, the threshold is input by the input 11 of the quantization selection circuit 10. The specific embodiment of the present invention is not limited.

In the embodiment of the present invention, the quantization circuit group 20 includes at least two quantization circuits, each of which corresponds to a quantization mode, wherein the optional quantization modes include: uniform quantization, A-rate quantization, and μ-rate quantization, etc., specifically adopting The manner of the present invention is not limited.

It can be seen that the embodiment of the invention described in FIG. 1 implements segmentation quantization on the prediction residual, that is, the matching quantization mode is selected for each segment according to the distribution rule of the prediction residual, and the quantization signal to noise ratio requirement can be met. Under the premise, the data compression rate is increased, that is, the bandwidth required for transmission is reduced.

Please refer to FIG. 2. FIG. 2 is a schematic structural diagram of another quantizer disclosed in the embodiment of the present invention. As shown in FIG. 2, the quantizer described in this embodiment can be built on the basis of the quantizer described in FIG. 1, in addition to the quantization selection circuit 10 and the quantization circuit group 20 described in FIG. The threshold calculation circuit 30 is included; wherein the embodiments of the quantization selection circuit 10 and the quantization circuit group 20 can be referred to the embodiment described in FIG.

The threshold calculation circuit 30 includes a first input terminal 31, a second input terminal 32, a third input terminal 33, a fourth input terminal 34, and an output terminal 35. The output 22 of the quantization circuit group 20 is connected to the third input 33 of the threshold calculation circuit 30; the output 35 of the threshold calculation circuit 30 is connected to the input 11 of the quantization selection circuit.

It should be noted that the first input end, the second input end, the third input end, the first output end, and the second output end of each hardware module in the embodiment of the present invention may be multiple in physical implementation. The different split ports may also be a merged port. The specific manner is selected according to the data transmission speed or the hardware implementation difficulty, and is not limited in the embodiment of the present invention.

Threshold value calculating circuit 30 in accordance with a second source data _{x l-1, x l-} 1 prediction residuals _{e l-1, e l-} 1 of the second quantization result u _l-1 and the target is determined quantization noise ratio SNR _t threshold, x _l-1 of the source data of a previous x _l; e _l-1 x _l-1 is the predicted value of x _l-1

Difference,

a predicted value obtained by linearly predicting x _l-1 ; wherein x _l-1 , e _l-1 , u _{l-1 ,} and SNR _t pass through the first input terminal 31 and the second input terminal of the threshold value calculation circuit 30, respectively. 32. The third input terminal 33 and the fourth input terminal 34 reach the threshold value calculation circuit 30; the threshold value reaches the input terminal 11 of the quantization selection circuit 10 via the output terminal 35 of the threshold value calculation circuit 30.

As an alternative embodiment, the threshold value of n-1 are values of τ ₁ ~ τ _n-1 consisting of a first set threshold, τ ₁ ~ τ _n-1 prediction residual range division n segments; the first threshold group is an average of n _τ threshold groups; wherein the interval between every two thresholds of the threshold group is determined according to the distribution of prediction residuals; and the distribution of prediction residuals is based on prior knowledge determine.

In the above embodiment, the threshold calculation circuit 30 obtains the threshold by:

First, by calculating the threshold set in the following manner: the prediction _{x l-1, x l-} 1 residuals e _l-1 and the second quantized result u _l-1 calculates the transmission end signal to noise ratio

Determining whether SNR _q is greater than SNR _t ; if SNR _q > SNR _t , increasing each threshold in the threshold group by δ; if SNR _q < SNR _t , decreasing each threshold in the threshold group by δ;

Loop calculation is performed _n τ subthreshold group acquires the calculation result threshold values _n τ group, _n τ averaged to obtain a set of threshold values a first threshold value group.

As an alternative embodiment, the quantizer described in FIG. 2 may further include a subtractor 40; the subtractor 40 includes a first input 41, a second input 42 and an output 43; the output 43 of the subtractor 40 The input terminal 11 of the quantization selection circuit 10 is connected.

It should be emphasized that the input end 11 of the quantization selection circuit 10 receives both the threshold and the prediction residual e _l , which may be the same input or two separate inputs in physical implementation. The specific implementation of the present invention is not limited in the embodiment of the present invention.

The subtractor 40 compares the predicted values of the first source data x _l and x _l

Subtracted to obtain the prediction residual x _l e _l;

X _l is predicted to be a value obtained by linear prediction; wherein, x _l and

Respectively via a first input 40 of the subtractor 41 and a second input terminal 42 to the subtracter 40; e _l subtracter 40 through the output terminal 43 reaches the quantization circuit 10 to select the input terminal 11.

As an alternative embodiment, the quantizer described in FIG. 2 may further include a linear predictor 50; the linear predictor 50 includes a first input 51, a second input 52, and an output 53; the output of the linear predictor The terminal 53 is connected to the input end 42 of the subtractor 40;

The linear predictor 50 calculates the predicted value of the first source data x _l based on the second source data x _l-1 and the second quantized result u _l-1

Wherein, x _l-1 and u _l-1 reach the linear predictor 50 via the first input 51 and the second input 52 of the linear predictor 50;

The output 53 of the linear predictor 50 reaches the second input 42 of the subtractor 40.

As an alternative embodiment, the linear predictor 50 may include a prediction coefficient calculation circuit 501 and a linear prediction circuit 502.

The prediction coefficient calculation circuit 501 includes an input terminal 5011 and an output terminal 5012. The linear prediction circuit 502 includes a first input terminal 5021, a second input terminal 5022, a third input terminal 5023, and an output terminal 5024.

Prediction coefficient calculation circuit 501 calculates the prediction coefficients, the prediction coefficients comprises coefficients a first prediction, the first prediction coefficients for the first source data x _l linear prediction coefficients for the prediction.

The above calculated prediction coefficients include:

Calculating the correlation function by using N _block source data before x _l ;

After f correlation functions are obtained, the f correlation functions are averaged to obtain an average correlation function, and the relationship between the average correlation function and the prediction coefficients is established by the minimum mean square error criterion to obtain the prediction coefficients.

The first prediction coefficient is passed to the first input 5021 of the linear prediction circuit 502 via the output 5012 of the prediction coefficient calculation circuit 501.

The linear prediction circuit 502 is based on the predicted values of the first prediction coefficient and the second source data.

And u _l-1 calculation

include:

Calculation

The product of the sum of u _l-1 and the first prediction coefficient as an intermediate amount;

After summing K intermediate quantities, summing K intermediate quantities to obtain

Where K is the order of linear prediction.

And u _l-1 respectively reach the linear prediction circuit 502 via the second input terminal 5022 and the third input terminal 5023 of the linear prediction circuit 502;

The output 5024 of the linear prediction circuit 502 reaches the second input 42 of the subtractor 40.

As an optional implementation manner, the quantizer described in FIG. 2 may further include a framing circuit 60; the framing circuit 60 includes a first input terminal 61, a second input terminal 62, a third input terminal 63, and a fourth input. The end 64 and the output 65; the output of the quantization selection circuit 10 further includes a first output 13 and a second output 14.

The first output terminal 13 and the second output terminal 14 of the quantization selection circuit 10 are connected to the first input terminal 61 and the second input terminal 62 of the framing circuit 60; the output terminal 22 of the quantization circuit group 20 is connected to the third of the framing circuit 60 The input terminal 63; the output terminal 5012 of the prediction coefficient calculation circuit 501 is connected to the fourth input terminal 64 of the framing circuit 60.

Quantization selection circuit 10 is also configured to obtain the location identification information u _l; location identification information is u _l e _l segment belongs; a first output terminal location identification information and the threshold value u _l respectively quantized selection circuit 10 and 13 a second output terminal 14 to the framing circuit 60; u _l quantized output circuit group 20 reaches the end 22 of the framing circuit 60; a first prediction coefficient prediction coefficient calculation circuit 501 via the output terminal 5012 reaches the framing circuit 60.

The framing circuit combines the position identification information of u _l , u _l , the threshold value and the first prediction coefficient into a data frame for transmission.

It can be seen that the embodiment of the invention described in FIG. 2 implements segmentation quantization on the prediction residual, that is, according to the distribution rule of the prediction residual, the matching quantization mode is selected for each segment, and the quantized signal to noise ratio requirement can be met. Under the premise, the data compression rate is increased, that is, the bandwidth required for transmission is reduced; in addition, the threshold of the prediction residual difference segment is determined according to the requirements of the target quantization signal to noise ratio to ensure that the quantized data can achieve ideal signal noise. ratio.

Please refer to FIG. 3. FIG. 3 is a schematic flowchart diagram of a quantization method according to an embodiment of the present invention. The quantization method described in FIG. 3 may include the following steps:

301, obtaining a first data source x _l e _l prediction residuals to a threshold.

In the embodiment of the present invention, the quantizer obtains the prediction residual e _{l of the} first source data x _l and a threshold, and the quantizer may be a circuit module in the terminal device as the data transmitting end.

In the embodiment of the present invention, the above e _l is a predicted value of x _l and x _l

Difference,

The predicted value obtained by linearly predicting x _l . The threshold value is n-1 first threshold groups each composed of values of τ ₁ to τ _n-1 , and the range of the prediction residual is divided into n segments.

In the embodiment of the present invention, the first threshold group may be pre-stored in the quantizer, or may be calculated according to data input in real time. As an optional implementation manner, the first threshold group is an average of n _τ threshold groups; wherein an interval between every two thresholds of the threshold group is determined according to a distribution of prediction residuals; and a prediction residual is distributed according to Prior knowledge is determined. According to the above features, the first threshold group can be calculated in the following manner:

First, the threshold group is calculated: the previous source data according to the first source data x _l : the second source data x _l-1 , the prediction residual e _{l-1 of the} second source data x _l-1 , and the first The second quantization result of the second source data u _l-1 calculates the signal to noise ratio of the transmitting end

Determining whether SNR _q is greater than SNR _t ; if SNR _q > SNR _t , increasing each threshold in the threshold group by δ; if SNR _q < SNR _t , decreasing each threshold in the threshold group by δ;

Loop is executed _n τ times and the threshold value set calculation process, the calculation result to obtain threshold values _n τ group, _n τ averaged to obtain a set of threshold values a first threshold value group.

302, e _l with a threshold value to determine the segment belongs e _l, e _l belongs segment determined e _l quantizing According target quantization.

In the embodiment of the present invention, different segments correspond to different quantization modes, wherein the quantized modes that can be used include: uniform quantization, A-rate quantization, and μ-rate quantization; and the same quantization method is used for all quantized residuals. The embodiment of the present invention can better match the distribution rule of the quantized residuals, and further reduce the bit width required for the quantization result.

303. Quantify e _l by using a target quantization manner to obtain a first quantization result u _l .

It can be seen that, by using the quantization method described in FIG. 3, the prediction residual is segment-quantized, that is, the matching quantization method is selected for each segment according to the distribution rule of the prediction residual, which can meet the requirements of the quantized signal to noise ratio. Under the premise, increase the data compression rate, that is, reduce the bandwidth required for transmission.

Please refer to FIG. 4. FIG. 4 is a schematic flowchart diagram of another quantization method according to an embodiment of the present invention. The quantization method described in FIG. 4 may include the following steps:

401. Calculate a prediction coefficient by using source data, where the prediction coefficient includes a first prediction coefficient.

In the embodiment of the present invention, the quantizer calculates the prediction coefficient by using the source data, and the quantizer may be a circuit module in the terminal device as the data transmitting end. In addition, the source data including the first source data x _l and the second source data x _l-1 is digitized data, for example, when the data transmission is performed, the data transmitting end needs to transmit. Quadrature modulated signal. The first prediction coefficient is a prediction coefficient that predicts the first source data x _l .

In the embodiment of the present invention, the predicted value of the first source data x _l can be obtained by linear prediction. Before the linear prediction is performed, the data transmitting end updates the prediction coefficient every time the f*N _block data is entered.

When the data transmitting end just starts to send data, the N _init data is obtained as the initial training sample. In order to reduce the computational complexity, the initial training sample is divided into F=N _init /N _block segments to perform correlation function calculation. Where the expression of the correlation function is

In the expression, k ₁ is the order of the correlation function, and N _block is the sample length of the source signal required to calculate the correlation function.

Then, for each piece of data of length N _block , a set of correlation functions R(0) to R(K) are calculated by means of shift multiplication or fast Fourier transform, and the obtained correlation function is stored and sent. Among the memory of the end, the storage structure diagram of the correlation function calculated for the initial training sample is as shown in Table 1:

R 1 (0)	R 2 (0)	...	R F (0)
R 1 (1)	R 2 (1)	...	R F (1)
...	...	...	...
R 1 (K)	R 2 (K)	...	R F (K)

Table 1 Storage structure diagram of the correlation function of the initial training sample

Considering that in the DPCM framework, the prediction coefficients need to be transmitted from the transmitting end to the receiving end, the transmission of the prediction coefficients will occupy the transmission bandwidth, so the prediction coefficient update step size cannot be too short. At the same time, it should be noted that the update of the prediction coefficients needs to be able to keep up with the time-varying characteristics of the correlation characteristics of the source data, so the update step of the prediction coefficients cannot be too long and needs to be compromised. In the implementation, we choose the update step size N _update . N _update should be less than N _init .

Similarly, the subsequent N _update data is divided into f=N _update /N _block segments, each segment length is N _block .

The process of subsequently calculating the prediction coefficients, including:

Assume that the currently input data is the first source data x _l , calculate the correlation function by using N _block source data before x _l , and cyclically calculate the correlation function, and store the obtained f group correlation function into Table 2 The related function shown is in the storage structure.

R f+1 (0)	R f+2 (0)	...	R F (0)	R 1 (0)	...	R f (0)
R f+1 (1)	R f+2 (1)	...	R F (1)	R 1 (1)	...	R f (1)
...	...	...	...	...	...	...
R f+1 (K)	R f+2 (K)	...	R F (K)	R 1 (K)	...	R f (K)

Table 2 related function storage structure diagram

Each time a set of related functions is evaluated, the array is shifted to the left overall, and the result of the new calculation is placed at the far right of the array. In the above table, the previous R _f+1 ～ R _F is the correlation function calculated from the f+1 to F data of the training sample, and the following R ₁ ～R _f are the f group correlations calculated by the subsequent set of data. The function, after entering the data, updates the array of related functions in this way. In the above manner, the data transmitting end stores only f correlation functions that are currently updated with prediction coefficients, the hardware overhead is moderate, and the hardware structure is easy to implement.

After f correlation functions are obtained, f related functions are averaged to obtain an average correlation function, and a relationship between the average correlation function and the prediction coefficient is established by a minimum mean square error criterion; wherein, the calculated average correlation function is R ( 0)～R(K), the relationship between the average correlation function and the prediction coefficient can be expressed by the following linear equations:

Therefore, according to the above linear equations, the prediction coefficients can be calculated.

402. Calculate a predicted value of the first source data x _l according to the first prediction coefficient, the second source data x _l-1, and the second quantization result u _l-1

In the embodiment of the present invention, the first source data x _l can be predicted by linear prediction. As shown in FIG. 4a, FIG. 4a is a schematic structural diagram of a linear predictor according to an embodiment of the present invention. As shown in FIG. 4a, the product of the previous source data x _l (ie, the second source data) x _l-1 and the prediction coefficient β ₁ is calculated as an intermediate amount; then the above calculated intermediate amount is repeatedly executed. a process of obtaining K intermediate quantities and summing K intermediate quantities to obtain a predicted value of the first source data x _l

Obtaining the above

After that, step 403 is performed to predict the first source data x _l and x _l

Subtracting to obtain the prediction residual e _{l of} x _l , and then outputting the prediction residual e _l to the encoder for quantization coding.

403. Predicting the first source data x _l and x _l

Subtracted to obtain the prediction residual x _l e _l.

According to the structural diagram of the linear predictor disclosed in FIG. 4a, the calculation formula of the prediction residual e _l can be:

Where K is the prediction order of the linear predictor, and its selection depends on the correlation characteristics of the source. It is necessary to analyze the relevant characteristics of the source data, that is, calculate the correlation function for the source data shift to select the appropriate interval. When the delay difference between the two sampled data is greater than the interval, the correlation between the two sampled data is lower than the expected threshold, and the interval value is the K value of the demand.

404. Determine a threshold according to the second quantization result u _l-1 and the target quantization signal to noise ratio SNR _t of the prediction residuals e _l-1 and e _l-1 of the second source data x _l-1 , x _l-1 .

Based on the linear prediction, the transmitting end needs to quantize the prediction residual. The selection of the quantization scheme depends on the statistical characteristics of the prediction residual. Therefore, the probability distribution of the prediction residual is first counted.

For example, if the probability density distribution of the prediction residual is shown in FIG. 4b, it can be seen from the figure that the distribution of the prediction residual substantially conforms to the characteristics of the Gaussian distribution, and the probability of occurrence of the small residual is large, and the large residual is large. The probability of occurrence is small. For the residual with smaller amplitude, the quantization can be performed with fewer bits, that is, the higher precision can be obtained, and for the residual with larger amplitude, more bits need to be used for quantization to ensure the quantization precision. Therefore, the prediction residual is segmented by the threshold, and each segment selects a quantization method suitable for the segment, which can further reduce the average bit width required for the quantization result.

In the embodiment of the present invention, the threshold value is n-1 first threshold groups respectively composed of values of τ ₁ ～τ _n-1 , and the value range of the prediction residual is divided into n segments, and each segment is predicted according to the prediction. The distribution of residuals is quantified in different ways.

Assuming that τ ₁ is the threshold with the smallest absolute value, the interval between the respective thresholds is d _i,j , then τ _k =τ ₁ +d _i,j ,k=2, . . . , n-1. Therefore, it is first necessary to determine the value of τ ₁ .

Please refer to FIG. 5. FIG. 5 is a schematic flowchart of calculating an initial value of τ ₁ according to an embodiment of the present invention. When the data transmission end just starts to send data, the initial τ ₁ can be calculated by the following steps:

501. Obtain N _init source data as an initial training sample.

502. Calculate a prediction residual of the source data in the initial training sample.

503. Calculate the residual power σ ² by using ΔPCM.

504. Construct a Gaussian distribution with a mean of 0 and a variance of σ ² .

505. Calculate the value of τ ₁ according to the Q function according to a preset ratio requirement.

After that, the threshold is updated once every time a fixed number of source data is input. Please refer to FIG. 6. FIG. 6 is a schematic flowchart diagram of a threshold update method according to an embodiment of the present invention. As an optional implementation manner, the threshold update can be performed as follows:

601, initialization parameter count=0, k=0. Initialize τ ₁ to τ _n-1 at the same time.

602. Obtain N _block source data, and then counter counter++.

603. Determine whether the counter count is equal to f. If it is equal to f, execute step 604, otherwise return to step 602.

604. Calculate the SNR _{q of the} data transmitting end, and count returns to zero, and the counter k++.

605, comparison SNR _q is greater than SNR _t, if the SNR _q> SNR _t, step 606 is performed; if SNR _q <SNR _t, step 607 is executed.

606. Increase each threshold in the threshold group by δ; wherein δ represents the size of each fine adjustment.

607. Decrease each threshold in the threshold group by δ; wherein δ represents the size of each fine adjustment.

608. Determine whether k is equal to n _τ . If yes, perform step 609. If no, return to step 602.

609. Find an average of n _τ threshold groups to update the threshold.

In the embodiment of the present invention, the threshold is periodically adjusted according to the signal-to-noise ratio of the data transmitting end to ensure that the quantization result can meet the preset signal-to-noise ratio requirement.

405, e _l compared to a threshold to determine whether the segment belongs e _l, e _l belongs segment determined e _l quantizing According target quantization.

For example, if the distribution of the prediction residuals conforms to the characteristics of the Gaussian distribution, the probability of occurrence of small residuals is large, while the probability of occurrence of large residuals is small. For the residual with smaller amplitude, the quantization can be performed with fewer bits, that is, the higher precision can be obtained, and for the residual with larger amplitude, more bits need to be used for quantization to ensure the quantization precision. Therefore, the prediction residual is segmented by the threshold, and each segment selects a quantization method suitable for the segment, which can further reduce the average bit width required for the quantization result.

406, using the target quantization quantizes e _l manner to obtain a first quantization result u _l.

For example, if two thresholds τ ₁ and τ _{2 are set} , the range of the prediction residual is divided into three segments, wherein the absolute value is smaller than τ ₁ and the absolute value is between τ ₁ and τ ₂ . For the medium signal, the absolute value is greater than τ ₂ for the large signal; the small signal is quantized by the logarithmic companding quantization of μ=1, and the quantization method for the medium signal is the logarithmic companding quantization of μ=30, large The signal is uniformly quantized; if the prediction residual e _l is a small signal, the logarithmic pressure expansion of μ=1 is used.

407. Obtain location identification information of u _l .

Embodiments of the present invention, location identification information u _l comprises a segment u _l corresponding e _l belongs, so as to be contained when u _l a data frame to the data receiving end, the data reception terminal belongs based on the threshold value and e _l of segment, for determining the data transmission terminal e _l quantizing embodiment, recovered by u _l e _l.

408, u _l, u _l of the location identification information, the first threshold value and a prediction coefficient data composed of frames for transmission.

It can be seen that, by using the quantization method described in FIG. 4, the prediction residual is segment-quantized, that is, the matching quantization mode is selected for each segment according to the distribution rule of the prediction residual, which can meet the requirements of the quantized signal to noise ratio. Under the premise, the data compression rate is increased, that is, the bandwidth required for transmission is reduced; in addition, the threshold of the prediction residual difference segment is determined according to the requirement of the target quantization signal to noise ratio to ensure that the quantized data can reach an ideal signal to noise ratio. .

Finally, the simulation is performed by using microwave 1024QAM orthogonal modulation data as source data (integer bit width is 3 bits) to specifically describe the above quantization method.

First, the parameters involved in the embodiment of the present invention are configured, and the parameter configuration during simulation is as shown in Table 3:

N init	300000	N update	100000
N block	1000	K	20
F	300	f	100

Table 3 simulation parameter configuration table

After that, two thresholds τ ₁ and τ _{2 are set} , and the range of the prediction residual is divided into three segments, wherein the absolute value is smaller than τ ₁ and the absolute value is between τ ₁ and τ ₂ . The signal, which has an absolute value greater than τ ₂ , is a large signal. The small and medium signals are logarithmically scaled similar to the μ law, and the large signals are uniformly quantized. Refer to Table 4, which is the parameter configuration information for segmentation quantization.

Table 4 Parametric Quantization Parameter Configuration

In this example, the following scheme is used to determine the quantization scheme for small and medium signals:

First, set different μ values for the quantization methods of medium and small signals, then evaluate the signal-to-noise ratio of small and medium signals separately, and find the most suitable μ value by dichotomy;

The y value is equally divided into 32 segments between [0, 1], and 33 boundary values y _i , i=0, 1, ..., 32, where y ₀ =0, y ₃₂ =1;

The slope of the polyline is determined according to the μ law formula, where the μ law formula is:

Taking into account the realization of the hardware, the 32-line line is used to approximate the μ law, and the slope of the 32-segment polyline of the small signal and the middle signal are respectively obtained.

with

i=1,...,32;

Calculate the corresponding x value by using the slope and y value of each segment of the polyline. The formula is:

x ₀ =0

The codebook1 and codebook2 are obtained by using the obtained boundary value of the x-axis, namely:

Calculate the quantized codebook of the small-medium signal in the positive part:

Codebook _s =codebook1*τ ₁

Codebook _m =codebook2*(τ ₂ -τ ₁ )+τ ₁

By inverting the obtained quantized codebook, a quantized codebook corresponding to the negative part is obtained, and a total of 64 values of the quantized codebook are formed.

The small signal and the middle signal are quantized by the above-described quantized codebook, and the large signal is quantized by means of uniform quantization. After the prediction residual is quantized, the coded transmission is performed. Because the segmentation quantization scheme is adopted, in order to ensure the correct decoding of the receiving end, the data frame after the framing needs to include the prediction coefficient, the position information of the segment where the prediction residual is located, Threshold, prediction residual.

According to the above method, the data transmission process is simulated, and the obtained signal-to-noise ratio is 55.0435dB, and the average bit of the transmitted data is 6.1780. Please refer to Table 5 for the simulation results obtained under the same conditions as CPRI compression and uniform quantization. Comparison of results:

Quantitative approach	Uniform quantization	CPRI compression	The solution of this embodiment
Bit width (average bit)	6bit	6bit	6.18bit
Signal to noise ratio	22.83	34.87	55.04
Compression ratio	0	25%	45%

Table 5 comparison of simulation results

As can be seen from the comparison of the simulation results in Table 5, compared with the uniform quantization and CPRI, the performance of the embodiment of the present invention in terms of quantizing the signal-to-noise ratio and the average bit is greatly improved, and the bandwidth required for digital transmission can be reduced.

After receiving the data frame, the data receiving end first de-frames to obtain a threshold, calculates a codebook according to the threshold, de-frames the prediction coefficient, and transmits the prediction coefficient to the linear predictor to obtain the predicted value of the source data; Obtaining the position information of the segment where the prediction residual is located, the decoder can be used to decode the quantization result; finally, the decoding result is added to the predicted value obtained by the linear predictor, and the signal sent by the data transmitting end can be recovered. source data.

In summary, the embodiment of the present invention implements segmentation quantization on the prediction residual, that is, selects a matching quantization mode for each segment according to the distribution rule of the prediction residual, which can satisfy the requirement of the quantized signal to noise ratio. To increase the data compression rate, that is, to reduce the bandwidth required for transmission.

One of ordinary skill in the art can understand all or part of the process of implementing the above embodiments, which can be completed by a computer program to instruct related hardware, the program can be stored in a computer readable storage medium, when the program is executed The flow of the method embodiments as described above may be included. The foregoing storage medium includes various media that can store program codes, such as a ROM or a random access memory RAM, a magnetic disk, or an optical disk.

Claims (14)

1. A quantizer, comprising:

   a quantization selection circuit and at least two quantization circuits; each quantization circuit corresponds to a quantization mode;

   An output end of the quantization selection circuit is connected to an input end of the quantization circuit;

   The quantization selection circuit for comparing a first data source e x _{l L} prediction residuals to a threshold to determine whether the segment belongs to _L e, e is determined according to the segment belongs to the _L e quantizing the target quantization _l manner, and the target quantization circuit E _l is output to the target for performing the quantization mode; the said X E _{l l l} the predicted value X

   

   Difference, said

   

   a prediction value obtained by linearly predicting the x _l ; the e _l reaches the quantization selection circuit via an input of the quantization selection circuit; the e _l reaches the output via an output of the quantization selection circuit Target quantization circuit;

   The target quantization circuit is configured to quantize the e _l to obtain a first quantization result u _l .
2. The quantizer according to claim 1, wherein said quantizer further comprises a threshold calculation circuit;

   An output end of the at least two quantization circuits is connected to an input end of the threshold calculation circuit; an output end of the threshold calculation circuit is connected to an input end of the quantization selection circuit;

   The threshold value calculating circuit according to a second source data x _l-1, x _l-1 the prediction residuals e _l-1, e _l-1 of the second quantization result and the target u _l-1 quantization noise ratio SNR _t determining the threshold, x _l-1 of the source data is a time prior to said x _l; e _l-1 is the x _l-1 and the x _l-1 of the Predictive value

   

   Difference, said

   

   a predicted value obtained by linearly predicting the x _l-1 ; wherein the x _l-1 , the e _l-1 , the u _l-1, and the SNR _{t are} subjected to the threshold calculation circuit The input arrives at the threshold calculation circuit; the threshold reaches the quantization selection circuit via an output of the threshold calculation circuit.
3. The quantizer according to claim 2, characterized in that the threshold value of n-1 are a first group of _n-1 threshold values τ ₁ ~ τ composition, the τ ₁ ~ τ _n-1 will The value range of the prediction residual is divided into n segments; the first threshold group is an average of n _τ threshold groups; wherein, the interval between every two thresholds of the threshold group is based on the distribution of the prediction residuals Determining; the distribution of the prediction residuals is determined based on prior knowledge;

   The threshold value calculating circuit according to a second source data x _l-1, x _l-1 the prediction residuals e _l-1, e _l-1 of the second quantization result and the target u _l-1 The quantized signal to noise ratio SNR _t determines the threshold, including:

   Calculate the threshold group:

   According to the x _l-1, x _l-1 the prediction residuals e _l-1 and the second quantization result u _l-1 calculates the transmission end signal to noise ratio

   

   Determining whether the SNR _q is greater than the SNR _t ; if SNR _q > SNR _t , increasing each threshold in the threshold group by δ; if SNR _q < SNR _t , decreasing each threshold in the threshold group δ;

   _N τ obtain results set threshold values, averaging the _n τ threshold values to obtain the first set of thresholds.
4. The quantizer according to any one of claims 1 to 3, wherein the quantizer further comprises a subtractor;

   An output end of the subtractor is connected to an input end of the quantization selection circuit;

   The subtractor is configured to compare the first source data x _l with the predicted value of the x _l

   

   Subtracted to obtain the prediction residual x _l e _l; the

   

   Is the predicted value obtained by the linear prediction x _l; wherein said x _l and the

   

   Via the subtractor reaches the input of a subtracter; e _l via said output terminal of said subtractor reaches the quantization selection circuit.
5. The quantizer according to claim 4, wherein said quantizer further comprises a linear predictor;

   An output of the linear predictor is coupled to an input of the subtractor;

   The linear predictor is configured to calculate a predicted value of the first source data x _l according to the second source data x _l-1 and the second quantization result u _l-1

   

   Wherein the x _l-1 and the u _l-1 reach the linear predictor via an input of the linear predictor;

   

   The subtractor is reached via the output of the linear predictor.
6. The quantizer of claim 5 wherein said linear predictor comprises:

   a prediction coefficient calculation circuit and a linear prediction circuit;

   The prediction coefficient calculation circuit is configured to calculate a prediction coefficient, where the prediction coefficient includes a first prediction coefficient, and the first prediction coefficient is a prediction coefficient that performs the linear prediction on the first source data x _l ;

   The calculating the prediction coefficient includes:

   Calculating a correlation function by using the N _block source data before the x _l ;

   Obtaining f related functions to average the f correlation functions to obtain an average correlation function, and establishing a relationship between the average correlation function and the prediction coefficients by a minimum mean square error criterion to obtain the prediction coefficient;

   The first prediction coefficient reaches the linear prediction circuit via an output end of the prediction coefficient calculation circuit;

   The linear prediction circuit is configured to predict a value according to the first prediction coefficient and the second source data

   

   And the u _l-1 calculation

   

   include:

   Calculate the

   

   a product of the sum of the u _l-1 and the first prediction coefficient as an intermediate amount;

   Summing the K intermediate quantities after obtaining the K intermediate quantities to obtain the

   

   Wherein K is an order of linear prediction;

   Said

   

   And the u _l-1 arrives at the linear prediction circuit via an input of the linear prediction circuit;

   

   The subtractor is reached via the output of the linear prediction circuit.
7. The quantizer according to claim 6, wherein said quantizer further comprises a framing circuit;

   An output end of the quantization selection circuit is connected to an input end of the framing circuit; an output end of the target quantization circuit is connected to an input end of the framing circuit; and an output end of the prediction coefficient calculation circuit is connected to the framing frame The input of the circuit;

   The quantization selection circuit is further configured to obtain identification information of the position of u _l; the location identification information u _l e _l comprises the segment belongs; u _l position of the identification information and the thresholds by An output of the quantization selection circuit reaches the framing circuit;

   The u _l reaches the framing circuit via an output end of the target quantization circuit;

   The first prediction coefficient reaches the framing circuit via an output end of the prediction coefficient calculation circuit;

   The framing circuit is configured to form the data frame of the u _l , the location identifier information of the u _l , the threshold, and the first prediction coefficient for transmission.
8. A method of quantification, comprising:

   Obtaining a prediction residual e _{l of the} first source data x _l and a threshold; the e _l is a predicted value of the x _l and the x _l

   

   Difference, said

   

   a predicted value obtained by linearly predicting the x _l ;

   The e _l to the threshold to determine the segment belongs e _l, e _l of the determination of the segment belongs e _l quantizing the target quantization According;

   The e _l is quantized by the target quantization method to obtain a first quantization result u _l .
9. The quantization method according to claim 8, wherein said first source data x _l e _l prediction residuals to a threshold value to determine the segment belongs before e _l, said method further comprising :

   Determining according to the second source data x _l-1 , the prediction residual e _{l-1 of} the x _l-1 , the second quantization result u _{l-1 of} the e _l-1 , and the target quantized signal to noise ratio SNR _t the threshold value; x _l-1 of the source data for the previous time the x _l, e _l-1 is the x _{l 1-a} and the predicted value of x _l-1

   

   Difference, said

   

   The predicted value obtained by linearly predicting the x _l-1 .
10. The quantization method according to claim 9, characterized in that the threshold value of n-1 are a first group of _n-1 threshold values τ ₁ ~ τ composition, the τ ₁ ~ τ _n-1 will The value range of the prediction residual is divided into n segments; the first threshold group is an average of n _τ threshold groups; wherein, the interval between every two thresholds of the threshold group is based on the distribution of the prediction residuals Determining; the distribution of the prediction residuals is determined based on prior knowledge;

    The second quantization result e _l-1 according to the second source data x _l-1 , the x _{l- 1} , the second quantization result u _{l-1 of} the e _l-1 , and the target quantized signal to noise ratio SNR _t determining the threshold, including:

    Calculate the threshold group:

    According to the x _l-1, x _l-1 the prediction residuals e _l-1 and the second quantization result u _l-1 calculates the transmission end signal to noise ratio

    

    Determining whether the SNR _q is greater than the SNR _t ; if SNR _q > SNR _t , increasing each threshold in the threshold group by δ; if SNR _q < SNR _t , decreasing each threshold in the threshold group δ;

    _N τ obtain results set threshold values, averaging the _n τ threshold values to obtain the first set of thresholds.
11. 8 ~ Before quantization method according to any one of claim 10, wherein said first source data x _l e _l prediction residuals to a threshold value to determine the segment belongs e _l The method further includes:

    Predicting the first source data x _l and the x _l

    

    Subtracted to obtain the prediction residual x _l e _l; the

    

    The predicted value obtained by linearly predicting the x _l .
12. The quantization method according to claim 11, wherein said first source data x _l and said predicted value of x _l

    

    Before subtracting to obtain the prediction residual e _{l of} the x _l , the method further includes:

    Calculating a predicted value of the first source data x _l according to the second source data x _l-1 and the second quantization result u _l-1

    
13. The quantization method according to claim 12, wherein the calculating the first source data x _l according to the second source data x _l-1 and the second quantization result u _l-1 Predictive value

    

    Previously, the method further includes:

    Calculating a prediction coefficient, the prediction coefficient including a first prediction coefficient, the first prediction coefficient being a prediction coefficient for predicting the first source data x _l ;

    The calculating the prediction coefficient includes:

    Calculating a correlation function by using the N _block source data before the x _l ;

    Obtaining f related functions to average the f correlation functions to obtain an average correlation function, and establishing a relationship between the average correlation function and the prediction coefficients by a minimum mean square error criterion to obtain the prediction coefficient;

    Calculating the predicted value of the first source data x _l according to the second source data x _l-1 and the second quantization result u _l-1

    

    include:

    Calculate the

    

    a product of the sum of the u _l-1 and the first prediction coefficient as an intermediate amount;

    Summing the K intermediate quantities after obtaining the K intermediate quantities to obtain the

    

    Where K is the order of linear prediction.
14. The method of quantizing according to claim 13, wherein the method further comprises:

    Obtaining the location identification information of the u _l and the threshold; the location identifier information of the u _l includes the segment to which the e _l belongs;

    The u _l , the location identification information of the u _l , the threshold, and the first prediction coefficient are combined into a data frame for transmission.

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